A wide range of applications exist for image data compression. Digitized images may be created in a variety of manners, such as via relatively simple digitizing equipment and digital camera, as well as by complex imaging systems, such as those used in medical diagnostic applications. Regardless of the environment in which the image data originates, the digital data descriptive of the images is stored for later reconstruction and display, and may be transmitted to various locations by networks, such as the Internet. Goals in digital image management include the efficient use of memory allocated for storage of the image data, as well as the efficient and rapid transmission of the image data for reconstruction. The latter goal is particularly important where large or complex images are to be handled over comparatively limited bandwidth networks. In the medical diagnostic imaging field, for example, very large image data sets may be available for transmission and viewing by a range of users, including those having limited access to very high bandwidths needed for rapid transmission of full detail images.
Picture archiving and communication systems, or PACS, have become an extremely important component in the management of digitized image data, particularly in the field of medical imaging. Such systems often function as central repositories of image data, receiving the data from various sources, such as medical imaging systems. The image data is stored and made available to radiologists, diagnosing and referring physicians, and other specialists via network links. Improvements in PACS have led to dramatic advances in the volumes of image data available, and have facilitated loading and transferring of voluminous data files both within institutions and between the central storage location or locations and remote clients.
A major challenge to further improvements in all image handling systems, from simple Internet browsers to PACS in medical diagnostic applications, is the handling of the large data files defining images. In the medical diagnostics field, depending upon the imaging modality, digitized data may be acquired and processed for a substantial number of images in a single examination, each image representing a large data set defining discrete picture elements or pixels of a reconstructed image. Computed Tomography (CT) imaging systems, for example, can produce numerous separate images along an anatomy of interest in a very short examination timeframe. Ideally, all such images are stored centrally on the PACS, and made available to the radiologist for review and diagnosis.
Various techniques have been proposed and are currently in use for analyzing and compressing large data files, such as medical image data files. Image data files typically include streams of data descriptive of image characteristics, typically of intensities or other characteristics of individual pixels in the reconstructed image. In the medical diagnostic field, these image files are typically created during an image acquisition or encoding sequence, such as in an x-ray system, a magnetic resonance imaging system, a computed tomography imaging system, and so forth. The image data is then processed, such as to adjust dynamic ranges, or to enhance certain features shown in the image, for storage, transmittal and display.
While image files may be stored in raw and processed formats, many image files are quite large, and would occupy considerable disc or storage space. The increasing complexity of imaging systems also has led to the creation of very large image files, typically including more data as a result of the useful dynamic range of the imaging system, the size of the matrix of image pixels, and the number of images acquired per examination.
In addition to occupying large segments of available memory, large image files can be difficult or time consuming to transmit from one location to another. In a typical medical imaging application, for example, a scanner or other imaging device will typically create raw data that may be at least partially processed at the scanner. The data is then transmitted to other image processing circuitry, typically including a programmed computer, where the image data is further processed and enhanced. Ultimately, the image data is stored either locally at the system, or in the PACS for later retrieval and analysis. In all of these data transmission steps, the large image data file must be accessed and transmitted from one device to another.
Current image handling techniques include compression of image data within the PACS environment to reduce the storage requirements and transmission times. Such compression techniques may, however, compress entire files, including descriptive header information that could be useful in accessing or correlating images for review. Moreover, current techniques do not offer sufficiently rapid compression and decompression of image files to satisfy increasing demands on system throughput rates and access times. Finally, alternative compression and decompression techniques do not offer the desired compression ratios, in combination with rapid compression and decompression in a client-server environment.
Another drawback of existing compression techniques is the storage, access and transmission of large data files even when a user cannot or does not desire to view the reconstructed image in all available detail. For example, in medical imaging, extremely detailed images may be acquired and stored, while a radiologist or physician who desires to view the images may not have a view port capable of displaying the image in the resolution in which they are stored. Thus, transmission of the entire images to a remote viewing station, in relatively time consuming operations, may not provide any real benefit and may slow reading or other use of the images.
There is a need, therefore, for an improved image data handling technique that provides for rapid transmission of image files and selective handling based on resolution characteristics of a desired viewport.